Curriculum alignment · Global

UNESCO AI Competency Framework for Students

We checked the LittleAIMaster Grade 1–12 AI course against all 36 rated requirements of this framework: 19 fully covered, 8 mostly covered, 9 partly covered. Open a requirement to see what learners do and the exact lessons.

75%
Fully covered 19Mostly covered 8Partly covered 9of 36 requirements8 taught earlier than the framework asks

Source documents (2)

AI competency framework for students
UNESCO (United Nations Educational, Scientific and Cultural Organization)
Published 2024 (launched Digital Learning Week, August 2024); ISBN 978-92-3-100709-5
Published August 2024

Alignment chart

Every UNESCO requirement, rated

5Taught at this grade5Some material5Grades the framework asks for
Open a requirement to see what learners do and the exact lessons.

Showing 36 of 36 requirements

AI competency framework for students

Human-centred mindset — Human agency (Understand)

Explain that AI is human-led and that decisions by AI creators shape how AI systems affect human rights, human-AI interaction, and society; recognise that AI cannot replace human thinking or intellectual development (CG4.1.1.1).

Framework grades 3–5, 6–8, 9–10
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Who Is Responsible?' and Grade 5 'Rules for AI', learners see AI as a tool you pick up, learn that lived experience cannot be supplied by AI, and that machine-made songs belong to the people behind them.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'You Control AI' and Grade 8 'Liam's Art Dilemma', learners sort tool-versus-boss habits, learn AI has no feelings, debate artists' work used without consent, and name lived experience as what AI cannot supply.

Our Grades 9–10 Fully covered
What learners do

In Grade 10 'AI and the Future of Work' and 'Privacy and AI', learners see AI as human-led collaboration and discuss privacy, autonomy and creativity tradeoffs; Grade 8 covers AI-art copyright and ownership.

Understand the necessity of exercising sufficient human control over AI, including the consequences when human oversight fails or is absent (CG4.1.1.2).

Framework grades 3–5, 6–8, 9–10
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Did It Really Help?', learners set a checkpoint of more than two misses in ten, inspect weekly results and choose a pause with a named human checker.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI in Healthcare' and 'Decision Tree Fun' and Grade 8 'Arjun's Smart Home', learners order TB X-ray oversight steps, tag decide-alone versus suggest-only by stakes, and require a manual override on every automation.

Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Responsible AI Development' and 'Deployment Basics', learners study continuous monitoring after launch, production red flags and drift, and ethics boards with authority to stop harmful projects.

Critically examine the dynamic relationship between human agency and machine agency, including cases where a degree of machine agency may be warranted (e.g. medical pattern detection) versus cases requiring human control (CG4.1.1.3).

Framework grades 3–5, 6–8, 9–10
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Who Is Responsible?' and Grade 5 'Rules for AI', learners see AI as a tool you pick up, learn that lived experience cannot be supplied by AI, and that machine-made songs belong to the people behind them.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'You Control AI' and Grade 8 'Liam's Art Dilemma', learners sort tool-versus-boss habits, learn AI has no feelings, debate artists' work used without consent, and name lived experience as what AI cannot supply.

Our Grades 9–10 Fully covered
What learners do

In Grade 10 'AI and the Future of Work' and 'Privacy and AI', learners see AI as human-led collaboration and discuss privacy, autonomy and creativity tradeoffs; Grade 8 covers AI-art copyright and ownership.

Ethics of AI — Embodied ethics (Understand)

Illustrate dilemmas around AI and identify the main reasons behind ethical conflicts that AI creators and companies face (e.g. data collection vs. ownership, profit vs. privacy, safety vs. speed of iteration) (CG4.1.2.1).

Framework grades 3–5, 6–8, 9–10
Partly covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Check a Real Rule', learners fill a stakeholder want-and-worry row, swap roles, explain the opposing view and debate a requirement.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI Debate Club', learners build claim-evidence-reasoning arguments and practise steelmanning; Grade 7 and Grade 8 lessons sort benefits by who gains and contrast different users' needs.

Our Grades 9–10 Partly covered
What learners do

In Grade 9 'The Biased Hiring Model', learners read a table of six stakeholders' arguments about a hiring screener, match arguments to stakeholders and pick the best rebuttal from options.

Still to add

Learners never represent stakeholders' goals themselves: no matrix is built, no steelman or reply is written, no debate is held and no outside views are sought.

Where it is taught

Discuss age-appropriate real-world cases around six core AI ethical principles — do no harm, proportionality, non-discrimination, sustainability, human determination, and transparency/explainability — and their personal implications (CG4.1.2.2).

Framework grades 3–5, 6–8, 9–10
Mostly covered
Our Grades 3–5 Mostly covered
What learners do

In Grade 5 'Judge One Machine' and 'Check a Real Rule', learners apply UNESCO criteria to a structured fictional case and critique real UNESCO guidance.

Still to add

Applying the named framework to a documented case of a real deployed system is not explicitly assigned.

Our Grades 6–8 Mostly covered
What learners do

In Grades 6-8, learners apply named frameworks such as the UNESCO Recommendation and the UAE Charter to cases like a school-gate face camera (Grade 7), an exam proctor and a hiring filter (Grade 8), choosing the broken principle and the fix.

Still to add

Framework verdicts are always picked from options; learners do not write their own justification, and bioethics is not used.

Where it is taught
Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Building Your AI Ethics Framework' and Grade 9 'The Biased Hiring Model', learners place a hiring AI in EU AI Act risk tiers and name the missing UNESCO principle, then write principles their own project meets.

Still to add

Applications to real cases are fixed-answer with pre-written feedback; learners never write a justified judgement on a real case.

Where it is taught

Internalise ethical principles on AI (human rights, data privacy, human agency, equity, inclusion, social justice, environmental sustainability) into reflective practices, e.g. advocating for inclusive AI interfaces and reporting discriminatory bias found in AI tools (CG4.1.2.3).

Framework grades 3–5, 6–8, 9–10
Mostly covered
Our Grades 3–5 Mostly covered
What learners do

In Grade 5 'Judge One Machine' and 'Check a Real Rule', learners apply UNESCO criteria to a structured fictional case and critique real UNESCO guidance.

Still to add

Applying the named framework to a documented case of a real deployed system is not explicitly assigned.

Our Grades 6–8 Mostly covered
What learners do

In Grades 6-8, learners apply named frameworks such as the UNESCO Recommendation and the UAE Charter to cases like a school-gate face camera (Grade 7), an exam proctor and a hiring filter (Grade 8), choosing the broken principle and the fix.

Still to add

Framework verdicts are always picked from options; learners do not write their own justification, and bioethics is not used.

Where it is taught
Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Building Your AI Ethics Framework' and Grade 9 'The Biased Hiring Model', learners place a hiring AI in EU AI Act risk tiers and name the missing UNESCO principle, then write principles their own project meets.

Still to add

Applications to real cases are fixed-answer with pre-written feedback; learners never write a justified judgement on a real case.

Where it is taught

AI techniques and applications — AI foundations (Understand)

Use examples of AI tools (facial recognition, recommenders, self-driving cars, loan-default prediction, etc.) to explain the definition and scope of AI — what AI is and is not — and identify the main categories of AI technologies in daily use (CG4.1.3.1).

We start Grade 2 · framework Grade 3Framework grades 3–5, 6–8, 9–10
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Robots' and 'AI at Work', learners sort machines into those told the steps and those shown examples, see that a toaster needs no AI, and learn AI as a machine that learns.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'What is AI?' and 'AI in Your Home', learners define AI and sort real gadgets as AI or not AI, using three levels, five clues and rule-based versus learning-based tests, giving reasons.

Our Grades 9–10 Fully covered
What learners do

In Grade 9 'Welcome, AI Engineers!', learners sort eight tasks as traditional programming or machine learning and judge eight products, such as Netflix, a calculator, Gmail and Tesla, as AI or not with reasons.

Develop an age-appropriate understanding of the three types of AI algorithms — supervised learning, unsupervised learning, and reinforcement learning — and how the data behind them is acquired and labelled (CG4.1.3.2).

We start Grade 2 · framework Grade 3Framework grades 3–5, 6–8, 9–10
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Remember: How Machines Learn', learners meet the names supervised, unsupervised and reinforcement learning, then sort six new jobs by the family that fits. Grade 2 'Rules or Examples?' already pairs the three ways with human learning.

Our Grades 6–8 Fully covered
What learners do

In Grade 7 'Supervised Learning Basics', learners meet definitions of supervised, unsupervised and reinforcement learning, group unlabelled drawings, and sort nine described problems into answer key, no teacher or scoreboard.

Where it is taught
  • Grade 7Unit 2: Teaching Machines › Supervised Learning Basics
    What is Supervised Learning?Learning Without LabelsGroup the Unlabelled DrawingsA Third Way: Learning From RewardsThree Ways to LearnKey VocabularyNani's Spotted LeavesInput or Label?The Two Piles Nobody Can NameTraining PhaseFair Exam or Not?Every Label Is a Human DecisionConcept MatcherOne Wrong Label Bends Every Prediction After ItClassification ExplainedBinary or Multi-class?Regression ExplainedWhere the Numbers Come From
Our Grades 9–10 Covered earlier · Grade 5
What learners do

In Grade 5 'How Machines Learn', learners see supervised, unsupervised and reinforcement learning defined as named examples, grouping and trying for a score, then sort six new jobs into the fitting family.

Gain interdisciplinary foundational knowledge for AI (e.g. artificial neural networks, strong vs weak AI, connections to STEM, languages and social studies) and reflect on AI's reciprocal impact on those subjects (CG4.1.3.3).

Framework grades 3–5, 6–8, 9–10
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Does It Understand?', learners classify current systems as narrow and tell them apart from hypothetical general and superintelligent systems.

Our Grades 6–8 Covered earlier · Grade 5
What learners do

In Grade 5 'One Job, Any Job, Smarter Than Us?', learners sort eight real and story systems into narrow, general and super, and decide that today's chatbot is narrow.

Our Grades 9–10 Covered earlier · Grade 5
What learners do

In Grade 5 'One Job, Any Job, Smarter Than Us?', learners sort eight real and story systems into narrow AI, general AI or superintelligence, and judge that today's chatbot is narrow. In Grade 6 they write why general AI is harder.

Where it is taught

Understand human roles across the AI life cycle (researchers, architecture/data engineers, beta testers, ethics/safety regulators, human-AI interface specialists, compliance auditors) and the main ethical issues in using data to train AI systems (CG4.1.3.4).

Framework grades 3–5, 6–8, 9–10
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 4 'How AI Is Built' and 'Thinking Well', learners match engineer, designer, checker, safety person, tester and collector to their roles, and see that builders decide where the classification line goes.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI Jobs of Tomorrow', learners map data scientist, AI trainer, ethics officer, safety tester and ML engineer to pipeline steps. The ethics reviewer asks who is treated unfairly.

Our Grades 9–10 Fully covered
What learners do

Grade 9 'Welcome, AI Engineers!' and Grade 10 'Responsible AI Development' cover the AI engineer role, labelling cost, bias auditing, model cards, diverse teams, accountable owners and monitoring after launch.

AI system design — Problem scoping (Understand)

Critically examine, from legal, ethical and logical perspectives, whether AI should or should not be used for a given real-world challenge, and define the boundaries, goals and constraints of a problem before attempting to train an AI model (CG4.1.4.1).

Framework grades 6–8, 9–10
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 8 'The AI Hammer', learners run a suitability checklist over data, complexity, cost and ethics, sort green-light from red-flag cases and write a justification.

Our Grades 9–10 Fully covered
What learners do

In Grade 8 'Maya's Discovery', learners use an AI suitability checklist, overkill test and cost-benefit analysis; Grade 10 'Spark of Inspiration' asks whether a project needs AI.

Practise skills to identify and scope a problem that could be solved by building or training a new AI model, based on a simulation project (CG4.1.4.2).

Framework grades 6–8, 9–10
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 8 'The Wrong Solution' and 'Capstone Project', learners scope WHO, WHAT, WHY and HOW MUCH and order the ask, collect, label, train, test, explain and deploy steps, including stakeholder interviews and data sourcing.

Our Grades 9–10 Fully covered
What learners do

In Grade 9 'The Failed Project' and Grade 10 'Spark of Inspiration' and 'Deployment Basics', learners order the six-stage lifecycle, scope a problem, acquire and explore data, choose and evaluate a model, and deploy and monitor it.

Develop skills to assess an AI system's needs for data, algorithms, programming languages, software, and computing hardware, including feasibility given regulatory/ethical restrictions and cost (CG4.1.4.3).

Framework grades 6–8, 9–10
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 8 'The Wrong Solution' and 'Capstone Project', learners scope WHO, WHAT, WHY and HOW MUCH and order the ask, collect, label, train, test, explain and deploy steps, including stakeholder interviews and data sourcing.

Our Grades 9–10 Fully covered
What learners do

In Grade 9 'The Failed Project' and Grade 10 'Spark of Inspiration' and 'Deployment Basics', learners order the six-stage lifecycle, scope a problem, acquire and explore data, choose and evaluate a model, and deploy and monitor it.

Human-centred mindset — Human accountability (Apply)

Understand that human accountability is a legal obligation of AI creators and AI service providers, who are answerable for safety incidents, ethical risks in design/training, and misuses of their AI service (CG4.2.1.1).

Framework grades 6–8, 9–10, 11–12
Partly covered
Our Grades 6–8 Mostly covered
What learners do

In Grade 8 'Testing AI Solutions' and 'How the World Governs AI', learners see who answers for an AI decision, which tools bring fines, and Italy's 2023 pause of ChatGPT, then apply these duties to a sample app.

Still to add

A provider's duty to report serious safety incidents is not stated; liability for misuse is touched only through China's minors rule and the Italy case, with no recall.

Where it is taught
Our Grades 9–10 Partly covered
What learners do

In Grade 10 'AI Governance and Regulation', learners read about accountability, EU AI Act high-risk duties such as testing, documentation and human oversight, and rules for providers of general-purpose AI.

Still to add

Providers' legal answerability for safety incidents and misuse is not taught, and learners do not apply these duties to a case; liability arrives in Grades 11-12.

Where it is taught
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'When AI Breaks the Law', learners study AI liability, disclaimers, DPDP duties, breach notification and a builder's responsibility for what ships, using real lawsuits.

Evaluate the consequences of institutional use of AI to make high-stakes decisions about people (e.g. profiling students, hiring, loan/assessment scoring) and understand why human accountability must not be ceded to AI in such decisions (CG4.2.1.2).

Framework grades 6–8, 9–10, 11–12
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'Decision Tree Fun' and 'AI in Healthcare', learners see loan approvals and medical triage where AI only suggests and a human decides, and debate AI essay marking.

Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Why AI Ethics Matters' and 'Explainable AI (XAI)', learners sort loans, hiring, diagnosis and sentencing by stakes with mandatory human review, and critique a black-box loan denial.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Algorithm That Discriminated' and Grade 11 'AI in Finance' and 'AI in Education', learners study hiring, credit scoring and grading, including right to explanation, appeals and human accountability.

Interrogate how automation of literature reviews, writing and artistic creation by AI may undermine human thinking and intellectual development, and identify concrete actions to protect themselves and peers from over-reliance on AI outputs (CG4.2.1.3).

We start Grade 4 · framework Grade 6Framework grades 6–8, 9–10, 11–12
Mostly covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'You Control AI', learners write one specific boundary to keep AI a helper, and learn about autocorrect dependence, productive struggle, and screen-free breaks and limits set in advance.

Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Choosing and Judging AI Tools', learners learn coach-not-ghostwriter strategies, sort eight AI uses as Coach or Ghostwriter, and use Prompt Lab on a real goal; in Grade 9 they prompt AI to quiz them on their own textbook paragraph.

Still to add

Learners only sort given scenarios; they do not list their own tasks, mark where AI would weaken them, or write personal guardrails, and creative work is not covered.

Where it is taught
Our Grades 11–12 Covered earlier · Grade 4
What learners do

In Grade 4 'Thinking Well', learners decide which parts of a task to hand to an AI and which to keep, order a plan that tries the work first, see how shortcuts cost learning, and word an ask for practice questions instead of answers.

Where it is taught

Ethics of AI — Safe and responsible use (Apply)

Carry out responsible AI practices in compliance with ethical principles and locally applicable regulations, iteratively building and following a personal checklist of lawful and responsible practices (e.g. protecting privacy/copyright, marking AI-generated content, avoiding disinformation, hate speech, or sensitive content about identifiable individuals) (CG4.2.2.1).

Framework grades 6–8, 9–10, 11–12
Partly covered
Our Grades 6–8 Covered earlier · Grade 3
What learners do

In Grade 3 'Who Owns It?', learners use six AI rules on one card: private things, ask or credit, say when AI made it, check before sharing, kind words, no fakes of real people. They sort cases and finish a poster honestly.

Where it is taught
Our Grades 9–10 Partly covered
What learners do

In Grade 10 'Choosing and Judging AI Tools', learners write an AI-use note for their own AI-assisted work; other lessons cover licences, consent for identifiable people and deepfake labelling through fixed-answer items.

Still to add

Learners never write or follow a personal lawful-practice checklist; copyright for AI output and avoiding hate speech or harassment in their own work are not covered.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'AI Art and Ethics', learners adapt a seven-line AI-practice checklist (no personal data, copyright, labelling AI output, verifying claims, no hateful content, consent for real people, crediting sources) and apply it to their capstone with evidence and one change.

Where it is taught

Gain an age-appropriate understanding of personal, legal and ethical responsibilities when using AI and reinforce self-disciplined behaviours around sensitive personal data, copyrighted materials, deepfake/identifiable-person imagery, and the spread of misinformation, disinformation or hate speech (CG4.2.2.2).

Framework grades 6–8, 9–10, 11–12
Partly covered
Our Grades 6–8 Covered earlier · Grade 3
What learners do

In Grade 3 'Who Owns It?', learners use six AI rules on one card: private things, ask or credit, say when AI made it, check before sharing, kind words, no fakes of real people. They sort cases and finish a poster honestly.

Where it is taught
Our Grades 9–10 Partly covered
What learners do

In Grade 10 'Choosing and Judging AI Tools', learners write an AI-use note for their own AI-assisted work; other lessons cover licences, consent for identifiable people and deepfake labelling through fixed-answer items.

Still to add

Learners never write or follow a personal lawful-practice checklist; copyright for AI output and avoiding hate speech or harassment in their own work are not covered.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'AI Art and Ethics', learners adapt a seven-line AI-practice checklist (no personal data, copyright, labelling AI output, verifying claims, no hateful content, consent for real people, crediting sources) and apply it to their capstone with evidence and one change.

Where it is taught

Categorise general safety risks of AI and typical AI incidents, deepen knowledge of human rights to data protection and privacy and creators' legal responsibility to collect data only with informed consent, and practise strategies to ensure personal data is collected, used, shared, archived and deleted only with consent (CG4.2.2.3).

Framework grades 6–8, 9–10, 11–12
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI and Privacy', Grade 7 'Teaching by Showing' and Grade 8 'Data for AI', learners study asking permission before recording classmates, community data licences, spotting privacy violations in datasets and data-protection rights.

Our Grades 9–10 Fully covered
What learners do

In Grade 9 'The Data Hunter' and Grade 10 'Privacy and AI', learners study consent, scraping etiquette, robots.txt, anonymity, attribution and data minimisation as rules for handling a dataset.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'AI Ethics in Practice' and Grade 12 'The Ethics Question' and 'When AI Breaks the Law', learners study consent, privacy, anonymisation, ownership and copyright with DPDP and GDPR duties, and propose lawful handling for each scenario.

AI techniques and applications — Application skills (Apply)

Acquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).

Framework grades 6–8, 9–10, 11–12
Mostly covered
Our Grades 6–8 Mostly covered
What learners do

In Grade 7 'Patterns in Data' and Python Lab Module 31 'Data Cleaning', learners clean a synthetic canteen log by removing duplicates, handling missing data, spotting an outlier, encoding and scaling.

Still to add

The dataset is invented, and the preprocessing is a whole-table cleaning exercise rather than preparation using training data only.

Our Grades 9–10 Fully covered
What learners do

In Grade 9 'The Messy Data Disaster' and Python Lab Module 31, learners clean a damaged real Iris extract, check types and missing targets, remove duplicates, split, and fit imputation, encoding and scaling on training rows only.

Our Grades 11–12 Fully covered
What learners do

Grade 9 chapters such as 'The Mysterious Gaps' and 'The Messy Data Monster' have learners handle missing values, outliers, encoding, scaling and leakage-safe train/test splitting with pandas and scikit-learn.

Where it is taught

Acquire age-appropriate technical skills in AI programming, understanding how supervised, unsupervised and reinforcement-learning algorithms scrape/process data, train, and function (CG4.2.3.2).

Framework grades 6–8, 9–10, 11–12
Partly covered
Our Grades 6–8 Partly covered
What learners do

In Grade 7 'Features and Attributes > Find the Nearest Neighbour' and Python Lab Module 8, learners implement three-nearest-neighbour voting on a supplied synthetic dataset.

Still to add

Regression fitting, iterative k-means and interpreting results across the named methods are not established in Grades 6-8.

Where it is taught
Our Grades 9–10 Fully covered
What learners do

In Grade 9 'ML's Hello World' and Python Lab Modules 36 and 37, learners fit and evaluate k-NN and k-means on Iris, interpret the labels and compare inertia, alongside regression in Module 35.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'AI in Retail', learners run k-means on shoppers and pick k by the elbow. Python Lab Modules 46 and 49 and 'AI in Healthcare' apply SVMs, DBSCAN and other methods, and Grade 12 uses nearest-neighbour matching.

Critically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).

Framework grades 6–8, 9–10, 11–12
Mostly covered
Our Grades 6–8 Mostly covered
What learners do

In Grade 7 'Teaching by Showing' and Grade 8 'The Global Goals Project', learners choose their own categories, draw examples and train a small classifier. Python Lab Module 48 and the Motion Trainer add gesture models.

Still to add

Building an application around the model is not guided, Scratch AI extensions are only described, and the Raise Your AI missions can be completed by tapping reveals.

Our Grades 9–10 Fully covered
What learners do

In Python Lab Module 44 and 45, learners reuse fixed pretrained digit feature weights, train a new classifier head and implement classify_drawing returning a predicted label and probability.

Our Grades 11–12 Fully covered
What learners do

Learners export Teachable Machine models into Scratch apps in Grade 8 (such as 'Arjun's Smart Game') and fine-tune pre-trained Keras models for custom classes in Grade 10.

Where it is taught

AI system design — Architecture design (Apply)

Acquire methodological and technical skills to evaluate frameworks/components (e.g. TensorFlow, PyTorch, Scikit-learn) and configure a scalable, maintainable, reusable prototype AI architecture (data, algorithm, model and interface layers) for a defined problem, considering open-source options (CG4.2.4.1).

Framework grades 9–10, 11–12
Partly covered
Our Grades 9–10 Partly covered
What learners do

In Grade 10 'The AI Blueprint', 'Model Optimization' and 'Deployment Basics', learners meet the model, optimisation and serving layers separately, including a cloud, edge or browser choice and a deployment plan.

Still to add

Learners do not design an end-to-end architecture with data, model and interface layers, choose frameworks against a budget, or estimate cost, compute or energy for their own system.

Our Grades 11–12 Partly covered
What learners do

Grade 11 'Beyond the Model' and Grade 12 'The Blueprint Before the Building' teach architecture layers, scaling, cost and energy per query, and learners answer design reviews of supplied systems. The 'Design an ML API' capstone covers the interface layer.

Still to add

Learners never specify data, model and interface layers for their own system, justify framework choices or set a compute, cost or energy budget.

Where it is taught

Acquire the interdisciplinary technical skills and project-management competencies needed to build a prototype AI system for a simple specific task (e.g. a chatbot), including leveraging computing resources, selecting/enhancing AI models (e.g. hyperparameter optimization), and balancing cost against computing needs and environmental impact (CG4.2.4.2).

Framework grades 9–10, 11–12
Partly covered
Our Grades 9–10 Partly covered
What learners do

In Grade 10 'The AI Blueprint', 'Model Optimization' and 'Deployment Basics', learners meet the model, optimisation and serving layers separately, including a cloud, edge or browser choice and a deployment plan.

Still to add

Learners do not design an end-to-end architecture with data, model and interface layers, choose frameworks against a budget, or estimate cost, compute or energy for their own system.

Our Grades 11–12 Partly covered
What learners do

Grade 11 'Beyond the Model' and Grade 12 'The Blueprint Before the Building' teach architecture layers, scaling, cost and energy per query, and learners answer design reviews of supplied systems. The 'Design an ML API' capstone covers the interface layer.

Still to add

Learners never specify data, model and interface layers for their own system, justify framework choices or set a compute, cost or energy budget.

Where it is taught

Human-centred mindset — AI society citizenship (Create)

Gain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).

Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Bias in AI Systems' and Grade 9 'The Biased Hiring Model', learners see COMPAS, the healthcare algorithm, Amazon hiring and Apple Card as documented unequal impact by demographic group.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The AI Radiologist' and 'The Algorithm That Discriminated', learners study healthcare AI that helps some patients while misdiagnosing women or failing rural clinics, plus COMPAS and hiring cases, using group-level metrics.

Where it is taught

Articulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).

We start Grade 6 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Building Your AI Ethics Framework', learners weigh innovation against social risk across user, creator and citizen roles and take responsibility for technology choices.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Right Thing', 'The Foundation of Trust' and 'Graduation Day', learners work through scenarios on workplace ethics, research integrity and digital citizenship, ending on the idea that their values decide what AI amplifies.

Reflect on personal goals and a lifelong-learning, self-actualization attitude toward AI as it becomes pervasive in work, life and social practices (CG4.3.1.3).

Framework grades 9–10, 11–12
Partly covered
Our Grades 9–10 Partly covered
What learners do

In Grade 9 'AI Careers: Who Builds, Shapes and Uses AI', learners explain one AI role and one sensible first step towards it, and order a starting path. Grade 10 lessons tell them about skills of tomorrow.

Still to add

Learners do not set and record personal AI-learning goals with a plan, checkpoints and review; the attitude is only told, and they write just a first step toward a role.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'Continuous Learning' and Grade 12 'Graduation and Beyond - Your AI Journey Continues', learners study continuous-learning habits, assess their own readiness and make plans for learning after graduation.

Ethics of AI — Ethics by design (Create)

Apply an integral, ethics-by-design set of principles across the whole AI life cycle, e.g. simulating the role of a 'chief ethics officer' drafting a checklist of ethical criteria to audit each step of an AI system's design (CG4.3.2.1).

Framework grades 9–10, 11–12
Partly covered
Our Grades 9–10 Partly covered
What learners do

In Grade 10 'Spark of Inspiration', learners sort a sample app's design choices against values and apply principles from their own country's list to their own idea, with one failing choice and its fix.

Still to add

Ethics is applied only at idea, problem and data definition; there is no affected-groups list for the learner's build and no well-being-focused design change.

Where it is taught
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Big Idea', learners keep an ethics log for their own assistant: one row each for problem, data, prompt, test and launch, naming affected people, a principle, a check, a design change and a re-test, with two well-being changes.

Where it is taught

Develop a critical attitude to ethics-by-design principles behind existing AI systems/algorithms by auditing selected tools, proposing an ethics label, and recommending remedies for identified violations of ethical principles or regulations (CG4.3.2.2).

Framework grades 9–10, 11–12
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Responsible AI Development', learners train a companion, measure accuracy, weakest category and balance, write a four-part model card and say what they would change to raise the weakest number.

Still to add

The taught card and the capstone have no explainability field and no privacy field.

Where it is taught
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Capstone Exhibition', learners train a drawing classifier, test it on other people's drawings, explain actual misses, and write an eight-section model card including explainability and privacy, then defend its headline number to a judge.

Where it is taught

Cultivate social responsibilities to uphold 'ethics by design' in regulations on AI — evaluating how selected local AI regulations align with ethics-by-design, and drafting proposals to modify or create new regulations governing ethics by design (CG4.3.2.3).

We start Grade 5 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Covered earlier · Grade 8
What learners do

In Grade 8 'Rules for the Next Loop', learners follow a draft, trial and review cycle and write three class AI rules covering who, for what and a check. In Grade 5 they check real UNESCO school guidance and rewrite a proposed law.

Where it is taught
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Council Decision', learners draft a policy proposal and a school AI-use guideline; a capstone requires consulting a student, a teacher and a parent. In Grade 11 'Draft Your Governance Clause' they write a clause.

Where it is taught

AI techniques and applications — Creating tools (Create)

Transfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).

We start Grade 7 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Python Lab Module 44 and 45, learners reuse fixed pretrained digit feature weights, train a new classifier head and implement classify_drawing returning a predicted label and probability.

Our Grades 11–12 Fully covered
What learners do

Learners export Teachable Machine models into Scratch apps in Grade 8 (such as 'Arjun's Smart Game') and fine-tune pre-trained Keras models for custom classes in Grade 10.

Where it is taught

Enhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).

We start Grade 7 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Python Lab Module 44 and 45, learners reuse fixed pretrained digit feature weights, train a new classifier head and implement classify_drawing returning a predicted label and probability.

Our Grades 11–12 Fully covered
What learners do

Learners export Teachable Machine models into Scratch apps in Grade 8 (such as 'Arjun's Smart Game') and fine-tune pre-trained Keras models for custom classes in Grade 10.

Where it is taught

Customize assessment methods/instruments to test self-crafted AI tools for robustness and ease of use, organise peer assessment and shared feedback, and build collaborative co-creator skills (CG4.3.3.3).

Framework grades 9–10, 11–12
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 9 'Science Fair Star', learners lock away test rows, compare the test score to a no-model baseline, name one limit and self-score against a guide. Grade 10 'Spark of Inspiration' covers values checks.

Still to add

Evaluation checks only the learner's own success test against a baseline; personal, social and environmental criteria are not in the Grade 9 rubric or scoring guide.

Where it is taught
Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'Building Your App' and Grade 12 'The Blueprint Before the Building', learners write test cases, run each twice, fix a line and re-run the suite. A Raise Your AI held-out test checks fairness across users.

Still to add

Environmental criteria (energy per use) and personal criteria (privacy, wellbeing) are not part of evaluating their own build, and the requirements document is not re-checked against it.

AI system design — Iteration and feedback loops (Create)

Practise critiquing AI systems by auditing whether a model enhances or weakens human capacities, agency and consciousness, checking the extent of its explainability and data-privacy protection, and measuring system performance and its broader societal/environmental impact (CG4.3.4.1).

Framework grades 9–10, 11–12
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Responsible AI Development', learners train a companion, measure accuracy, weakest category and balance, write a four-part model card and say what they would change to raise the weakest number.

Still to add

The taught card and the capstone have no explainability field and no privacy field.

Where it is taught
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Capstone Exhibition', learners train a drawing classifier, test it on other people's drawings, explain actual misses, and write an eight-section model card including explainability and privacy, then defend its headline number to a judge.

Where it is taught

Build technical and social-responsibility skills to make decisions on optimizing, reconfiguring, or shutting down an AI system based on test results and user feedback (three scenarios: optimize datasets/model/interface; reconfigure problem-scoping/architecture; or shut down and remediate where the system violates human rights or harms vulnerable groups) (CG4.3.4.2).

Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Responsible AI Development' and 'Deployment Basics', learners study continuous monitoring after launch, production red flags and drift, and ethics boards with authority to stop harmful projects.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Safety Lab' and 'AI That Takes Action', learners study and judge agent guardrails such as least privilege, human approval, budget limits and logging, plus kill switches and human sign-off gates.

Nurture a self-identity as a co-creator and 'driver' of the next generation of AI technology, critically analyse the long-term impacts of AI systems on social relations and behaviours, and discuss how regulations/policies should adapt to enhance AI governance (e.g. by engaging with local/online communities of AI co-creators) (CG4.3.4.3).

We start Grade 5 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Covered earlier · Grade 8
What learners do

In Grade 8 'Rules for the Next Loop', learners follow a draft, trial and review cycle and write three class AI rules covering who, for what and a check. In Grade 5 they check real UNESCO school guidance and rewrite a proposed law.

Where it is taught
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Council Decision', learners draft a policy proposal and a school AI-use guideline; a capstone requires consulting a student, a teacher and a parent. In Grade 11 'Draft Your Governance Clause' they write a clause.

Where it is taught

School and system rules (not rated)

These apply to schools and education systems (hours, delivery, assessment, staffing, age rules). A course cannot meet them on its own.

  • The companion Teachers framework (5 aspects x 3 levels = 15 competency blocks: teachers' mindset, AI ethics, AI foundations, AI pedagogy, AI for professional development) requires teachers to be able to use AI ethically, effectively, safely and responsibly to enhance teaching and learning — implying students should experience AI-enhanced pedagogy delivered under a human-centred, ethics-first approach, though the Teachers document itself does not specify separate student-facing curricular goals beyond deferring to the Students framework.

Bring the course to your school or region

Schools, school groups, resellers and partners: we will walk you through the alignment for your board and the lessons behind it.

For schoolsFor partners and resellersContact us

See how the course maps to the other 13 curricula on the curriculum alignment overview, or browse the course grade by grade.

Our own reading of our own course, ratings reviewed 22 October 2026. It is not an endorsement, certification or accreditation by any authority named here, and it makes no claim about learning results.